Below example was made by R.

In [1]:
library(GEOquery)
library(affy)
library(dendextend)
library(biomaRt)
library(limma)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: ‘BiocGenerics’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from ‘package:stats’:

    IQR, mad, xtabs

The following objects are masked from ‘package:base’:

    anyDuplicated, append, as.data.frame, as.vector, cbind, colnames,
    do.call, duplicated, eval, evalq, Filter, Find, get, grep, grepl,
    intersect, is.unsorted, lapply, lengths, Map, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unlist, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Setting options('download.file.method.GEOquery'='auto')
Setting options('GEOquery.inmemory.gpl'=FALSE)

Welcome to dendextend version 1.1.2

Type ?dendextend to access the overall documentation and
browseVignettes(package = 'dendextend') for the package vignette.
You can execute a demo of the package via: demo(dendextend)

More information is available on the dendextend project web-site:
https://github.com/talgalili/dendextend/

Contact: <tal.galili@gmail.com>
Suggestions and bug-reports can be submitted at: https://github.com/talgalili/dendextend/issues

			To suppress the this message use:
			suppressPackageStartupMessages(library(dendextend))


Attaching package: ‘dendextend’

The following object is masked from ‘package:stats’:

    cutree


Attaching package: ‘limma’

The following object is masked from ‘package:BiocGenerics’:

    plotMA

In [2]:
# Below code should be executed to download files.

#getGEOSuppFiles("GSE59880")
#untar("GSE59880/GSE59880_RAW.tar", exdir = "GSE59880/CEL")

list.files("GSE59880")

#list.files("GSE59880/CEL")
Out[2]:
  1. 'CEL'
  2. 'filelist.txt'
  3. 'GSE59880_RAW.tar'

I use 'tar' and 'gzip' commands to uncompress files manually instead of '/bin/gtar' command.

In [3]:
celfiles <- list.files("GSE59880/CEL")
celfiles <- unlist(celfiles)
In [4]:
rawData <- ReadAffy(filenames = celfiles,celfile.path='/home/vagrant/maybegit/tensorflow-exercise/R/GSE59880/CEL/')

'/home/vagrant/maybegit/tensorflow-exercise/R/GSE59880/CEL/' is my directory path.

In [5]:
edata_raw <- exprs(rawData)

dim(edata_raw)

edata_raw[1:10,]

sum(is.na(edata_raw))

par(mfrow=c(1,2))

boxplot(log(edata_raw[,1:15]), main="old", col=2,range=0 )

boxplot(log(edata_raw[,16:30]), main="young", col=2,range=0 )

par(mfrow=c(1,1))

hist(log(edata_raw[,1]))
Out[5]:
  1. 1354896
  2. 30
Out[5]:
GSM1448899_o9.CELGSM1448900_o15.CELGSM1448901_o2.CELGSM1448902_o8.CELGSM1448903_o7.CELGSM1448904_o5.CELGSM1448905_o11.CELGSM1448906_o6.CELGSM1448907_o14.CELGSM1448908_o1.CELGSM1448909_o4.CELGSM1448910_o13.CELGSM1448911_o12.CELGSM1448912_o3.CELGSM1448913_o10.CELGSM1448914_y14.CELGSM1448915_y13.CELGSM1448916_y3.CELGSM1448917_y2.CELGSM1448918_y12.CELGSM1448919_y9.CELGSM1448920_y6.CELGSM1448921_y5.CELGSM1448922_y1.CELGSM1448923_y15.CELGSM1448924_y8.CELGSM1448925_y4.CELGSM1448926_y11.CELGSM1448927_y7.CELGSM1448928_y10.CEL
1133 82 89128177181168199151297181182176117179105 86122101104124 89122 87128100173 84111 95
2 9521 6111 7469 92381294110435116451183710463133481130212461105221170411896 79481162512266109411210611824129761225611975128981595410835139321174411176
3189118149158237209268293241328222272278249180148142118145144126 99116130148152162109128131
4 9413 7508 7865 95941323411041120611191010667133481127112414111951204412451 83651220512865117731269412189137101299913757124841619010845147651276511783
5 72 74 78104106103146136130131102108111 94 96 89 98 80 83108102 84 86 92102 90 80105100 80
6154 93 78 95126140173166156223161127145144126 74 91 85 89 78 99 74104 81 86 77120 78 77 85
7 9545 6393 7694 91081334710857118301200910544131201141812395105821227812575 81941180112374114311234512125128661206012251127631555210754140301184611362
8199105112147195218220235201282202178204208183131104136122103100108 93137117147128123109109
9 9221 6547 7371 89191279510912117871179810473131341103412650106121189612717 78071154512453110341216811905130371214811846124231563210705139651123110725
10206102 99122180171231217215290181170190185183131105121 96 88107 89106145124120149125121 91
Out[5]:
0
In [6]:
mm = log(edata_raw[0:50000,1]+1) - log(edata_raw[0:50000,2]+1)
aa = log(edata_raw[0:50000,1]+1) + log(edata_raw[0:50000,2]+1)
plot(aa,mm,col=2)
In [7]:
normData <- rma(rawData)
normData

dim(normData)
Warning message:
: replacing previous import by ‘utils::tail’ when loading ‘hgu133plus2cdf’Warning message:
: replacing previous import by ‘utils::head’ when loading ‘hgu133plus2cdf’
Background correcting
Normalizing
Calculating Expression
Out[7]:
ExpressionSet (storageMode: lockedEnvironment)
assayData: 54675 features, 30 samples 
  element names: exprs 
protocolData
  sampleNames: GSM1448899_o9.CEL GSM1448900_o15.CEL ...
    GSM1448928_y10.CEL (30 total)
  varLabels: ScanDate
  varMetadata: labelDescription
phenoData
  sampleNames: GSM1448899_o9.CEL GSM1448900_o15.CEL ...
    GSM1448928_y10.CEL (30 total)
  varLabels: sample
  varMetadata: labelDescription
featureData: none
experimentData: use 'experimentData(object)'
Annotation: hgu133plus2 
Out[7]:
Features
54675
Samples
30
In [8]:
head(exprs(normData))

edata <- exprs(normData)

boxplot(edata[,1:30],main="expression data",col=2)
Out[8]:
GSM1448899_o9.CELGSM1448900_o15.CELGSM1448901_o2.CELGSM1448902_o8.CELGSM1448903_o7.CELGSM1448904_o5.CELGSM1448905_o11.CELGSM1448906_o6.CELGSM1448907_o14.CELGSM1448908_o1.CELGSM1448909_o4.CELGSM1448910_o13.CELGSM1448911_o12.CELGSM1448912_o3.CELGSM1448913_o10.CELGSM1448914_y14.CELGSM1448915_y13.CELGSM1448916_y3.CELGSM1448917_y2.CELGSM1448918_y12.CELGSM1448919_y9.CELGSM1448920_y6.CELGSM1448921_y5.CELGSM1448922_y1.CELGSM1448923_y15.CELGSM1448924_y8.CELGSM1448925_y4.CELGSM1448926_y11.CELGSM1448927_y7.CELGSM1448928_y10.CEL
1007_s_at7.8755088.1981918.3967028.2760558.4132988.1473018.0501748.3567638.1455277.8740978.3693808.0915578.5320488.1067048.5484387.9681597.8386297.8201387.4833297.8640727.9201087.6871128.0083967.9223507.6142688.0170337.8402988.1899627.7430207.817590
1053_at5.9878345.7957816.1672836.0182626.2476545.7744196.1970516.3145686.0602185.5964675.8524816.2306395.9625146.1194866.0869585.8981475.9780726.1425465.9497546.0276135.8623915.6869125.7821415.8868025.7551215.7084055.9950295.9773805.5249405.738622
117_at4.6244414.8217094.6025314.6631854.3436684.5780354.3434194.5253144.4650464.6626884.6624644.5424774.5086834.5377974.7444694.5538454.8290614.7073184.8452804.7750714.8232414.6537714.6060544.7398744.9162434.8549894.5995974.8264914.4673574.614699
121_at8.0772708.2809667.9277487.8985427.7533208.1039257.6934117.4714968.2312358.3045438.2722498.0478258.1032808.0104797.8584288.1118708.2234517.7075567.3419697.4138217.4099188.2650008.1800998.1714477.6201278.1437497.4887758.3461307.5685587.573623
1255_g_at3.3750053.0542723.0948273.1266822.9325953.1042913.0511013.0413813.1902053.3678773.2315513.2786792.9751042.9255633.0735873.1040263.1207422.9529633.2334163.1202872.9768593.3792843.1992463.2238162.9065583.2214993.0667883.2416293.1447622.960716
1294_at5.9896695.9201235.9747755.7699336.1071315.7845975.5084135.5622885.8467615.8254365.9852945.8273295.9608775.8488676.0780405.8993685.9579575.7812255.7295355.6276185.6329436.0653165.8351036.1145645.7684485.9502135.6773396.1740835.4698585.752867
In [10]:
geoMat <- getGEO("GSE59880",destdir = "/home/vagrant/maybegit/tensorflow-exercise/R/GSE59880")
ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE59nnn/GSE59880/matrix/
Found 1 file(s)
GSE59880_series_matrix.txt.gz
File stored at: 
/home/vagrant/maybegit/tensorflow-exercise/R/GSE59880/GPL570.soft
In [12]:
pD.all <- pData(geoMat[[1]])

head(pD.all)
names(pD.all)

##pD <- pD.all[, c("title", "geo_accession", "characteristics_ch1.1","characteristics_ch1.2","characteristics_ch1.3","characteristics_ch1.4","platform_id"  )]


pD <- pD.all[, c("title", "geo_accession", "characteristics_ch1.1","characteristics_ch1.2")]

names(pD)[c(3,4)] <- c("age", "fitness")
## head(pD)

##pD$age
pD$age <- sub("^age: ","", pD$age)
##pD$age

##pD$fitness 
pD$fitness <- sub("^aerobic fitness: ", "", pD$fitness)
##pD$fitness

changeName <- function(x) {

    print("function starts.... object length is")
    print(length(x))

    for (i in 1:length(x)) {

      x[i] <- substr(x[i], start=1, stop=10) 

      print(x[i])
    }

  return(x)

}

sampleNames(normData) <- changeName(sampleNames(normData))

sampleNames(normData)
Out[12]:
titlegeo_accessionstatussubmission_datelast_update_datetypechannel_countsource_name_ch1organism_ch1characteristics_ch1characteristics_ch1.1characteristics_ch1.2characteristics_ch1.3characteristics_ch1.4treatment_protocol_ch1growth_protocol_ch1molecule_ch1extract_protocol_ch1label_ch1label_protocol_ch1taxid_ch1hyb_protocolscan_protocoldescriptiondata_processingplatform_idcontact_namecontact_emailcontact_institutecontact_addresscontact_citycontact_zip/postal_codecontact_countrysupplementary_filedata_row_count
GSM1448899o9GSM1448899Public on Aug 05 2015Jul 29 2014Aug 06 2015RNA1resting skeletal muscleHomo sapiensgroup: oldage: 63aerobic fitness: 3.50gender: maletissue: skeletal muscleSubjects were physiologically assessed at baselineNot applicabletotal RNATRIzolbiotinAffymetrix Standard protocol.9606Affymetrix Standard protocol.Affymetrix Standard protocol.no additional informationThe data was analyzed with the affy package from Bioconductor using the RMA algorithm.GPL570Jamie,,Timmonsjamie.timmons@xrgenomics.comXRGenomics31-35,Kirby streetLondonEC1N 8TEUnited Kingdomftp://ftp.ncbi.nlm.nih.gov/pub/geo/DATA/supplementary/samples/GSM1448nnn/GSM1448899/GSM1448899_o9.CEL.gz54675
GSM1448900o15GSM1448900Public on Aug 05 2015Jul 29 2014Aug 06 2015RNA1resting skeletal muscleHomo sapiensgroup: oldage: 77aerobic fitness: 2.74gender: maletissue: skeletal muscleSubjects were physiologically assessed at baselineNot applicabletotal RNATRIzolbiotinAffymetrix Standard protocol.9606Affymetrix Standard protocol.Affymetrix Standard protocol.no additional informationThe data was analyzed with the affy package from Bioconductor using the RMA algorithm.GPL570Jamie,,Timmonsjamie.timmons@xrgenomics.comXRGenomics31-35,Kirby streetLondonEC1N 8TEUnited Kingdomftp://ftp.ncbi.nlm.nih.gov/pub/geo/DATA/supplementary/samples/GSM1448nnn/GSM1448900/GSM1448900_o15.CEL.gz54675
GSM1448901o2GSM1448901Public on Aug 05 2015Jul 29 2014Aug 06 2015RNA1resting skeletal muscleHomo sapiensgroup: oldage: 59aerobic fitness: 2.63gender: maletissue: skeletal muscleSubjects were physiologically assessed at baselineNot applicabletotal RNATRIzolbiotinAffymetrix Standard protocol.9606Affymetrix Standard protocol.Affymetrix Standard protocol.no additional informationThe data was analyzed with the affy package from Bioconductor using the RMA algorithm.GPL570Jamie,,Timmonsjamie.timmons@xrgenomics.comXRGenomics31-35,Kirby streetLondonEC1N 8TEUnited Kingdomftp://ftp.ncbi.nlm.nih.gov/pub/geo/DATA/supplementary/samples/GSM1448nnn/GSM1448901/GSM1448901_o2.CEL.gz54675
GSM1448902o8GSM1448902Public on Aug 05 2015Jul 29 2014Aug 06 2015RNA1resting skeletal muscleHomo sapiensgroup: oldage: 63aerobic fitness: 2.42gender: maletissue: skeletal muscleSubjects were physiologically assessed at baselineNot applicabletotal RNATRIzolbiotinAffymetrix Standard protocol.9606Affymetrix Standard protocol.Affymetrix Standard protocol.no additional informationThe data was analyzed with the affy package from Bioconductor using the RMA algorithm.GPL570Jamie,,Timmonsjamie.timmons@xrgenomics.comXRGenomics31-35,Kirby streetLondonEC1N 8TEUnited Kingdomftp://ftp.ncbi.nlm.nih.gov/pub/geo/DATA/supplementary/samples/GSM1448nnn/GSM1448902/GSM1448902_o8.CEL.gz54675
GSM1448903o7GSM1448903Public on Aug 05 2015Jul 29 2014Aug 06 2015RNA1resting skeletal muscleHomo sapiensgroup: oldage: 63aerobic fitness: 2.32gender: maletissue: skeletal muscleSubjects were physiologically assessed at baselineNot applicabletotal RNATRIzolbiotinAffymetrix Standard protocol.9606Affymetrix Standard protocol.Affymetrix Standard protocol.no additional informationThe data was analyzed with the affy package from Bioconductor using the RMA algorithm.GPL570Jamie,,Timmonsjamie.timmons@xrgenomics.comXRGenomics31-35,Kirby streetLondonEC1N 8TEUnited Kingdomftp://ftp.ncbi.nlm.nih.gov/pub/geo/DATA/supplementary/samples/GSM1448nnn/GSM1448903/GSM1448903_o7.CEL.gz54675
GSM1448904o5GSM1448904Public on Aug 05 2015Jul 29 2014Aug 06 2015RNA1resting skeletal muscleHomo sapiensgroup: oldage: 61aerobic fitness: 1.97gender: maletissue: skeletal muscleSubjects were physiologically assessed at baselineNot applicabletotal RNATRIzolbiotinAffymetrix Standard protocol.9606Affymetrix Standard protocol.Affymetrix Standard protocol.no additional informationThe data was analyzed with the affy package from Bioconductor using the RMA algorithm.GPL570Jamie,,Timmonsjamie.timmons@xrgenomics.comXRGenomics31-35,Kirby streetLondonEC1N 8TEUnited Kingdomftp://ftp.ncbi.nlm.nih.gov/pub/geo/DATA/supplementary/samples/GSM1448nnn/GSM1448904/GSM1448904_o5.CEL.gz54675
Out[12]:
  1. 'title'
  2. 'geo_accession'
  3. 'status'
  4. 'submission_date'
  5. 'last_update_date'
  6. 'type'
  7. 'channel_count'
  8. 'source_name_ch1'
  9. 'organism_ch1'
  10. 'characteristics_ch1'
  11. 'characteristics_ch1.1'
  12. 'characteristics_ch1.2'
  13. 'characteristics_ch1.3'
  14. 'characteristics_ch1.4'
  15. 'treatment_protocol_ch1'
  16. 'growth_protocol_ch1'
  17. 'molecule_ch1'
  18. 'extract_protocol_ch1'
  19. 'label_ch1'
  20. 'label_protocol_ch1'
  21. 'taxid_ch1'
  22. 'hyb_protocol'
  23. 'scan_protocol'
  24. 'description'
  25. 'data_processing'
  26. 'platform_id'
  27. 'contact_name'
  28. 'contact_email'
  29. 'contact_institute'
  30. 'contact_address'
  31. 'contact_city'
  32. 'contact_zip/postal_code'
  33. 'contact_country'
  34. 'supplementary_file'
  35. 'data_row_count'
[1] "function starts.... object length is"
[1] 30
[1] "GSM1448899"
[1] "GSM1448900"
[1] "GSM1448901"
[1] "GSM1448902"
[1] "GSM1448903"
[1] "GSM1448904"
[1] "GSM1448905"
[1] "GSM1448906"
[1] "GSM1448907"
[1] "GSM1448908"
[1] "GSM1448909"
[1] "GSM1448910"
[1] "GSM1448911"
[1] "GSM1448912"
[1] "GSM1448913"
[1] "GSM1448914"
[1] "GSM1448915"
[1] "GSM1448916"
[1] "GSM1448917"
[1] "GSM1448918"
[1] "GSM1448919"
[1] "GSM1448920"
[1] "GSM1448921"
[1] "GSM1448922"
[1] "GSM1448923"
[1] "GSM1448924"
[1] "GSM1448925"
[1] "GSM1448926"
[1] "GSM1448927"
[1] "GSM1448928"
Out[12]:
  1. 'GSM1448899'
  2. 'GSM1448900'
  3. 'GSM1448901'
  4. 'GSM1448902'
  5. 'GSM1448903'
  6. 'GSM1448904'
  7. 'GSM1448905'
  8. 'GSM1448906'
  9. 'GSM1448907'
  10. 'GSM1448908'
  11. 'GSM1448909'
  12. 'GSM1448910'
  13. 'GSM1448911'
  14. 'GSM1448912'
  15. 'GSM1448913'
  16. 'GSM1448914'
  17. 'GSM1448915'
  18. 'GSM1448916'
  19. 'GSM1448917'
  20. 'GSM1448918'
  21. 'GSM1448919'
  22. 'GSM1448920'
  23. 'GSM1448921'
  24. 'GSM1448922'
  25. 'GSM1448923'
  26. 'GSM1448924'
  27. 'GSM1448925'
  28. 'GSM1448926'
  29. 'GSM1448927'
  30. 'GSM1448928'
In [13]:
group <- ifelse((pD$age)>50, "old", "young")

pD <- cbind(pD,group)

pData(normData) <- pD
In [14]:
mod = model.matrix(~ pData(normData)$group)
fit_limma = lmFit(exprs(normData),mod)
ebayes_limma = eBayes(fit_limma)
In [15]:
limma_pvals = topTable(ebayes_limma,adjust.method="BH",sort.by="none",number=dim(edata)[1])

dim(limma_pvals)

head(limma_pvals[limma_pvals$adj.P.Val < 0.05,])

sum(limma_pvals$adj.P.Val < 0.05)

cgenes <- rownames(limma_pvals[limma_pvals$adj.P.Val < 0.05,])

length(cgenes)
Removing intercept from test coefficients
Out[15]:
  1. 54675
  2. 6
Out[15]:
logFCAveExprtP.Valueadj.P.ValB
1007_s_at-0.37648528.037207-5.2912018.203546e-060.00027416193.574788
117_at0.14579764.6479612.7743060.0091030680.04173321-3.051468
1487_at0.32567548.9251535.0679461.577324e-050.00043077022.947034
1552261_at0.19854585.1172352.7475990.0097235860.04367705-3.111429
1552263_at0.28080533.3691884.4452879.650026e-050.0015487151.213105
1552272_a_at-0.24514225.733616-3.8774630.00048573770.004990174-0.3229463
Out[15]:
12888
Out[15]:
12888
In [17]:
limma_pvals_test2 = topTable(ebayes_limma,adjust.method="hochberg",sort.by="logFC",number=dim(edata)[1])

tgenes <- rownames(limma_pvals_test2[limma_pvals_test2$adj.P.Val < 0.05,])

length(tgenes)
#sum(tgenes %in% genes)
Removing intercept from test coefficients
Out[17]:
796
In [26]:
#library(xlsx)
#library(xsv)

data <- read.csv("/home/vagrant/maybegit/tensorflow-exercise/R/GSE59880/aging_genes.csv")

head(data)
Out[26]:
Notes.takes.from.Pubmed.for.the.top.150.genes.in.the.prototype.healthy.ageing.diagnostic.gene.listXX.1X.2X.3ExerciseExercise.1MuscleBrainLongevity.GWASHorvathHannum
1HU133 Probeset_IDGene SymbolRatio of Y:0 muscleGene TitleBiology notesRegulated by aerobic exerciseregulated by resistance trainingPlos Age corr listSibille Age Brain CorrPERLS Age SNPSDNAm with ageDNAm with age
2236278_atHIST1H3EdownHistone cluster 1, H3eReplication-dependent histone; core component of nucleosome; reduced gene expression in aged mice in hippocampusNONONONONONONO
3204974_atRAB3AdownRAB3A, member RAS oncogene familyGTPase/Ca+ signalling; age-related changes in human brain; Alzheimer's Disease link;NONONONONONONO
4205050_s_atMAPK8IP2downmitogen-activated protein kinase 8 interacting protein 2AKA JIP2; scaffold protein that binds many JNK isoforms; regulates MAPK8; APP and Glucose - biochem of 'ageing diseases'NONONONONONONO
5206416_atZNF205downzinc finger protein 205DNA binding protein; regulates human M-LPH - potentially oxidative stress relatedNONONONONONONO
6229730_atSMTNL2downsmoothelin-like 2JNK substrateNOYES, downNONONONONO
In [28]:
genes <- as.vector(as.matrix(data))
sum(cgenes %in% genes)
Out[28]:
150
In [29]:
genes_age <- exprs(normData)[rownames(exprs(normData))  %in% genes,]

head(genes_age)
Out[29]:
GSM1448899GSM1448900GSM1448901GSM1448902GSM1448903GSM1448904GSM1448905GSM1448906GSM1448907GSM1448908GSM1448909GSM1448910GSM1448911GSM1448912GSM1448913GSM1448914GSM1448915GSM1448916GSM1448917GSM1448918GSM1448919GSM1448920GSM1448921GSM1448922GSM1448923GSM1448924GSM1448925GSM1448926GSM1448927GSM1448928
1556095_at3.8872414.5604025.9185754.9478285.4681364.8760055.0216195.5553394.5402793.4797114.5468464.4424604.9784314.8797053.2298283.9811822.9044022.8682113.0323963.1137973.2411822.8213373.1421762.7041843.1076862.7572583.4504922.8946353.2328092.481630
201592_at10.1849810.6566910.7864310.7979410.4834010.3987110.4933610.8434110.7472410.2345210.5615710.6826610.5578510.7232910.6288410.8690011.0186311.0217711.3477611.1232310.9869710.8797810.8738310.9781611.1299411.0459811.1579111.0310711.0827911.29522
202312_s_at7.0481456.9079376.8935206.8238086.6002696.7749266.6873276.7649666.2049037.0082846.7014846.9049146.5564926.6101486.6775276.9640657.1401177.5971587.6450827.5325137.4648087.3284807.2018687.4798337.4479087.2169957.5971587.2816977.2108357.362451
202588_at8.5719438.4120828.9047628.3012938.3629558.3913718.1593198.5284408.6979688.1039578.3623738.2372448.1395358.3849008.1414558.6252078.9718309.1709869.4838349.0041949.1063658.6787478.6427399.1371059.4038078.9840679.3389089.1099099.1368909.300256
203055_s_at6.8071277.1074246.9813727.0932606.6578846.7879936.5472026.6626726.5182396.5710116.9491537.0284906.8020626.8469717.1855887.0099167.2536127.3653647.4830867.3869587.5059727.3177937.2066797.2822567.4774017.3052377.3565887.1965997.3524547.480548
203842_s_at6.9629797.3323387.1995467.2045067.0889606.8977956.5240606.9288296.2791556.7103206.5986996.6176286.3748956.7690557.1150317.2290487.5323477.7471537.7382997.8123927.8094787.4069007.1852667.3440707.9679467.4342227.6070717.5052077.7395187.988916
In [30]:
boxplot(genes_age)
In [31]:
heatmap(genes_age)
In [32]:
dist1 = dist(t(genes_age))

hclust1 = hclust(dist1)
plot(hclust1)
In [33]:
dend = as.dendrogram(hclust1)
dend = color_labels(hclust1,2,col=1:2)
plot(dend)
In [40]:
Sys.info()
Out[40]:
sysname
'Linux'
release
'3.19.0-25-generic'
version
'#26~14.04.1-Ubuntu SMP Fri Jul 24 21:16:20 UTC 2015'
nodename
'vagrant'
machine
'x86_64'
login
'vagrant'
user
'vagrant'
effective_user
'vagrant'
In [ ]: